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"Robotics Experiments."
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Precision programming of roving robots : project-based fundamentals of wheeled, legged and hybrid mobile robots
\"This book is designed primarily as a laboratory operations manual for fundamental mechatronics and robotics experiential and project-based learning. It is also ordered in that starting with the Tricycle Robot, students build up their knowledge and experience of programming to be able to tackle the Rickshaw Robot and finally the most complex robot, i.e., the Hexapod Robot. The book is aimed at university and college students; however, with robotics curricula extending down into lower grades this book can also be very useful for teachers at any school level. Furthermore, the book provides useful ideas for driverless vehicles and robots, as well as for educators who are developing practical project-based teaching and learning modules\"-- Provided by publisher.
Book
A Robotics Experimental Design Method Based on PDCA: A Case Study of Wall-Following Robots
There is a lack of research that proposes a complete and interoperable robotics experimental design method to improve students’ learning outcomes. Therefore, this study proposes a student-oriented method based on the plan-do-check-act (PDCA) concept to design robotics experiments. The proposed method is based on our teaching experience and multiple practical experiences of allowing students to do hands-on experiments. It consists of eight steps, mainly including experimental goals, experimental activities, robot assembly, robot control, in-class evaluation criteria, and after-class report requirements. The after-class report requirements designed in the proposed method can help students improve their report-writing abilities. A wall-following robotics experiment designed using the PDCA method is proposed, and some students’ learning outcomes and after-class reports in this experiment are presented to illustrate the effectiveness of the proposed method. This experiment also helps students to understand the fundamental application of multi-sensor fusion technology in designing an autonomous mobile robot. We can see that the proposed reference examples allow students to quickly assemble two-wheeled mobile robots with four different sensors and to design programs to control these assembled robots. In addition, the proposed in-class evaluation criteria stimulate students’ creativity in assembling different wall-following robots or designing different programs to achieve this experiment. We present the learning outcomes of three stages of the wall-following robotics experiment. Three groups of 42, 37, and 44 students participated in the experiment in these three stages, respectively. The ratios of the time required for the robots designed by students to complete the wall-following experiment, less than that of the teaching example, are 3/42 = 7.14%, 26/37 = 70.27%, and 44/44 = 100%, respectively. From the comparison of learning outcomes in the three stages, it can be seen that the proposed PDCA-based design method can indeed improve students’ learning outcomes and stimulate their active learning and creativity.
Journal Article
Build your own robot science fair projects
\"Design and build your own robots, RC cars, motors, and more with these prize-winning science fair ideas. Photos, diagrams, and step-by-step instructions make it easy\"-- Page 4 of cover.
Book
Machine Learning-aided Process Design for Formulated Products
Robotic experiments were coupled with the previously published Thompson Sampling Efficient Multiobjective Optimization (TS-EMO) algorithm, using a batch sequential design approach, in order to optimize the composition and the process conditions of a commercial formulated product. The algorithm was trained with a previously collected data set used to optimize the formulation without taking into account the influence of the process conditions. The target was to obtain a clear homogeneous formulation within a certain viscosity range, minimizing the cost of the adopted ingredients. The GP surrogate models used in the algorithm were found suitable to model the complex unknown relationship between the input space and the outputs of interest, identifying suitable samples with a general decrease in the formulation price, needed mixing power, and process time. The proposed methodology can lead to quicker product design and therefore can generate considerable profit increase with an early product release time.
Book Chapter
Robot experiments
\"Presents several science projects dealing with robots\"--Provided by publisher.
Book
LARS: A Light-Augmented Reality System for Collective Robotic Interaction
Collective robotics systems hold great potential for future education and public engagement; however, only a few are utilized in these contexts. One reason is the lack of accessible tools to convey their complex, embodied interactions. In this work, we introduce the Light-Augmented Reality System (LARS), an open-source, marker-free, cross-platform tool designed to support experimentation, education, and outreach in collective robotics. LARS employs Extended Reality (XR) to project dynamic visual objects into the physical environment. This enables indirect robot–robot communication through stigmergy while preserving the physical and sensing constraints of the real robots, and enhances robot–human interaction by making otherwise hidden information visible. The system is low-cost, easy to deploy, and platform-independent without requiring hardware modifications. By projecting visible information in real time, LARS facilitates reproducible experiments and bridges the gap between abstract collective dynamics and observable behavior. We demonstrate that LARS can serve both as a research tool and as a means to motivate students and the broader public to engage with collective robotics. Its accessibility and flexibility make it an effective platform for illustrating complex multi-robot interactions, promoting hands-on learning, and expanding public understanding of collective, embodied intelligence.
Journal Article
Design of a Cost-Effective Ultrasound Force Sensor and Force Control System for Robotic Extra-Body Ultrasound Imaging
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety. However, existing robotic ultrasound systems rely heavily on expensive commercial force sensors or the integrated sensors of commercial robotic arms, limiting their accessibility. To address these challenges, we developed a cost-effective, lightweight, 3D-printed force sensor and a hybrid position–force control strategy tailored for robotic ultrasound scanning. The system integrates patient-to-robot registration, automated scanning path planning, and multi-sensor data fusion, allowing the robot to autonomously locate the patient, target the region of interest, and maintain optimal contact force during scanning. Validation was conducted using an ultrasound-compatible abdominal aortic aneurysm (AAA) phantom created from patient CT data and healthy volunteer testing. For the volunteer testing, during a 1-min scan, 65% of the forces were within the good image range. Both volunteers reported no discomfort or pain during the whole procedure. These results demonstrate the potential of the system to provide safe, precise, and autonomous robotic ultrasound imaging in real-world conditions.
Journal Article
The Moral Machine experiment
With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To address this challenge, we deployed the Moral Machine, an online experimental platform designed to explore the moral dilemmas faced by autonomous vehicles. This platform gathered 40 million decisions in ten languages from millions of people in 233 countries and territories. Here we describe the results of this experiment. First, we summarize global moral preferences. Second, we document individual variations in preferences, based on respondents’ demographics. Third, we report cross-cultural ethical variation, and uncover three major clusters of countries. Fourth, we show that these differences correlate with modern institutions and deep cultural traits. We discuss how these preferences can contribute to developing global, socially acceptable principles for machine ethics. All data used in this article are publicly available.
Responses from more than two million people to an internet-based survey of attitudes towards moral dilemmas that might be faced by autonomous vehicles shed light on similarities and variations in ethical preferences among different populations.
Journal Article
A Human Support Robot for the Cleaning and Maintenance of Door Handles Using a Deep-Learning Framework
The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.
Journal Article
Experiment-free exoskeleton assistance via learning in simulation
Exoskeletons have enormous potential to improve human locomotive performance
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–
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. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws
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. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals.
A learning-in-simulation framework for wearable robots uses dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments to assist versatile activities.
Journal Article